A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding
Abstract EEG-based emotion decoding is essential for unveiling neural mechanisms of emotion and has applications in mental health and human-machine interaction. However, existing datasets for EEG-based emotion decoding are limited to a single context of emotion elicitation. The ability of emotion de...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05349-2 |
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| author | Xin Xu Xinke Shen Xuyang Chen Qingzhu Zhang Sitian Wang Yihan Li Zongsheng Li Dan Zhang Mingming Zhang Quanying Liu |
| author_facet | Xin Xu Xinke Shen Xuyang Chen Qingzhu Zhang Sitian Wang Yihan Li Zongsheng Li Dan Zhang Mingming Zhang Quanying Liu |
| author_sort | Xin Xu |
| collection | DOAJ |
| description | Abstract EEG-based emotion decoding is essential for unveiling neural mechanisms of emotion and has applications in mental health and human-machine interaction. However, existing datasets for EEG-based emotion decoding are limited to a single context of emotion elicitation. The ability of emotion decoding methods to generalize across different contexts remains underexplored. To address this gap, we present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of specific emotion categories was validated through subjective reports. To validate the potential of cross-context emotion decoding, we implemented a support vector machine with L1 regularization, achieving accuracies of 66.7% for binary classification (positive vs. negative emotions) and 28.9% for seven-category emotion classification, both significantly above chance levels. The EmoEEG-MC dataset serves as a foundational resource for understanding the neural substrates of emotion and enhancing the real-world applicability of affective computing. |
| format | Article |
| id | doaj-art-5ad7be0933bd46fc8e2f0015d374ae33 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-5ad7be0933bd46fc8e2f0015d374ae332025-08-20T03:37:19ZengNature PortfolioScientific Data2052-44632025-07-0112111310.1038/s41597-025-05349-2A Multi-Context Emotional EEG Dataset for Cross-Context Emotion DecodingXin Xu0Xinke Shen1Xuyang Chen2Qingzhu Zhang3Sitian Wang4Yihan Li5Zongsheng Li6Dan Zhang7Mingming Zhang8Quanying Liu9Department of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Psychological and Cognitive Sciences, Tsinghua UniversityDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyAbstract EEG-based emotion decoding is essential for unveiling neural mechanisms of emotion and has applications in mental health and human-machine interaction. However, existing datasets for EEG-based emotion decoding are limited to a single context of emotion elicitation. The ability of emotion decoding methods to generalize across different contexts remains underexplored. To address this gap, we present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of specific emotion categories was validated through subjective reports. To validate the potential of cross-context emotion decoding, we implemented a support vector machine with L1 regularization, achieving accuracies of 66.7% for binary classification (positive vs. negative emotions) and 28.9% for seven-category emotion classification, both significantly above chance levels. The EmoEEG-MC dataset serves as a foundational resource for understanding the neural substrates of emotion and enhancing the real-world applicability of affective computing.https://doi.org/10.1038/s41597-025-05349-2 |
| spellingShingle | Xin Xu Xinke Shen Xuyang Chen Qingzhu Zhang Sitian Wang Yihan Li Zongsheng Li Dan Zhang Mingming Zhang Quanying Liu A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding Scientific Data |
| title | A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding |
| title_full | A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding |
| title_fullStr | A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding |
| title_full_unstemmed | A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding |
| title_short | A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding |
| title_sort | multi context emotional eeg dataset for cross context emotion decoding |
| url | https://doi.org/10.1038/s41597-025-05349-2 |
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